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SpaceBender denoises spatial transcriptomics data by leveraging spatial information, improving biological signal clarity and statistical significance in simulations and real tissues.

The paper analyzes proteomics dataset reuse using PRIDE download statistics and a semi-supervised LLM-based framework to classify download sources.

This paper introduces a proximity-aware graph attention network to infer cell-cell communication from spatial transcriptomics data, achieving improved performance over existing methods.

This paper improves AlphaFold3 by using customized MSAs and templates, achieving higher accuracy in structure prediction for monomers, multimers, and protein-ligand complexes.

DeepBranchAI reduces annotation time for 3D branching network segmentation by using a cascade workflow that transforms sparse labels into comprehensive training data.

This paper introduces an autonomous LLM agent platform for computational binder design and ADC prioritization, achieving accurate classification across oncology targets with minimal human intervention.

This paper introduces ContinualTCR, a continual learning framework for TCR peptide binding prediction that reduces catastrophic forgetting when adapting to new epitopes.

This paper adapts a modernized BERT-style model to genomic sequence modeling, achieving strong efficiency and performance, and releases it as an open baseline.

The paper introduces MIRNAPEX, a framework that accounts for alternative polyadenylation to better understand miRNA differential expression by integrating target-gene expression and 3'UTR isoform usage.

CHORD integrates cross-species single-cell transcriptomic data to learn gene, cell, and cell-type representations, enabling cell-type annotation and cross-species comparison of developmental and phenotypic patterns.

The paper integrates cross-dataset proteomic profiles using differential change directionality, identifying a central hub for doxorubicin treatment and associating proteome clusters with tumor stage.

BioAutoML-FAST provides an automated ML platform for biological sequence analysis, offering reusable models and benchmarking across genomic, transcriptomic, and proteomic tasks.

CryptoBank identifies cryptic binding sites in proteins using structural alignments and a machine learning model, enabling prediction from sequence data with high precision.

The paper improves sequence-to-expression models by incorporating chromatin accessibility, showing better performance in predicting gene expression and cell type-specific patterns.

RNABag is a transcriptome foundation model that generalizes across biopsy modalities, achieving superior performance in cancer detection and patient stratification.

BioEngine enables biologists to run, adapt, and deploy bioimage AI models on various hardware by interacting with an AI agent through a user-friendly interface.

This study compares two single-cell immune profiling technologies, evaluating their performance in transcriptomic and T cell receptor features across human PBMC samples.

The paper introduces RiboSeek, a framework for RNA 3D structure search using geometric alphabets, achieving fast and accurate comparisons.